Sunday, November 11, 2012

More Stats Supporting Justin Verlander for Cy Young

Most readers of this blog are aware of the limitations of ERA in evaluating pitcher performance. Two of the biggest issues are:

(1) ERA gives pitchers full credit/blame for results of batted balls in
play despite the fact that they share that responsibility with
fielders. For example, a pitcher with a strong defense behind him will
tend to give up fewer hits (and thus fewer runs) than if he had a poor
defense behind him and this will deflate his ERA. .

(2) ERA gives pitchers full responsibility for sequencing or timing of
events, that is, it assumes that they can control when they give up hits
and walks. For example, if a pitcher pitches extraordinarily well with
runners in scoring position in a given year, he will have a lower ERA
than if he had a typical year in those situations. Additionally, a
pitcher who tends to bunch base runners together in single innings will
have a higher ERA than if he had a typical year distributing base
runners more evenly.

In reality, pitchers have limited control over both the number of batted
balls that drop for hits and sequencing of events. Thus, Defense
Independent Pitching Statistics (DIPS) such as FIP, xFIP, tERA and
SIERA have been developed to remove some of the noise of ERA. DIPS are
based on things that pitchers do control for the most part - walks,
hit batsmen, strikeouts, home runs and types of batted balls (ground
balls , fly balls, line drives, pop flies).

Because they are based on things that pitchers essentially control, the
DIPS metrics are said to be better measures of true talent than ERA.
As a result, they are also better than ERA at predicting future
performance. However, they only measure a portion of a pitcher's talent
and should be used as complements to ERA rather than as replacements.

More and more fans are becoming comfortable with the DIPS theory, but it is
still a really difficult concept to get across to the mainstream. If
you ever try to explain FIP or any other DIPS statistic to the
uninitiated, you will probably find that they are skeptical of a
pitching statistic which ignores hits. They are not likely to buy into
it even if they realize the limitations of ERA.

So, rather than asking fans to take the big leap from ERA to FIP, why
not meet them half way? Instead of removing hit prevention and
sequencing in one step, it might be better to remove one factor at a
time. Bill James did that with his Component ERA
(ERC). Applying the runs created methodology to pitchers, he
determined what a pitcher's ERA should have been based on walks, hit
batsmen, strikeouts, homers AND hits allowed. I'm going to look at
some similar statistics here based on more modern measures such as
linear weights and Base Runs.

We often use Weighted On-Base Average (wOBA) to measure overall hitting
performance and it can also be used for pitchers. The American League
wOBA Against (wOBAA) leaders are shown in Table 1 below. Tigers ace
Justin Verlander led the league with a .268 wOBAA in 2012. Teammates Doug Fister (.302) and Max Scherzer (.316) also finished among the top twenty starters.

It's always good to convert to runs allowed when trying to evaluate
pitchers, so I'll do that next. The Base Runs measure was created by
David Smythe in the early 1990s.
It is based on the idea that we can estimate team runs scored if we know
the number of base runners, total bases, home runs and the typical
score rate (the score rate is the percentage of base runners that score
on average. Base Runs also works well for individual pitchers. The
complete formula can be found here.

Justin Verlander had 78 Base Runs Against in 238 1/3 innings this year. This means that he should have allowed an estimated 78 runs based
on the number of base runners, total bases and home runs he
allowed. He allowed 81 actual runs, so runs scored against him
at a slightly higher rate than you would expect (although it was pretty close). The small difference could possibly be
due to bad defense, unfortunate timing or just bad luck on locations of
batted balls.

Verlander had 40 Base Runs Above Average (RAA) which means that he saved the Tigers an estimated 40 runs compared to the average
pitcher in the same number of innings. This was the best total in the AL. Note that this number is adjusted for home ballpark (using five-year ballpark factors developed by Brandon Heipp of Walk Like a Sabermetrician).

Finally, Table 3 shows that Verlander allowed an AL best 2.91 Base Runs per nine
innings (BsR9). Again, this is adjusted for ballpark. The BsR9 statistic is not a novel idea as Mr. Heipp has been using Base Runs in this way for a while. About 93% of runs are earned, so you could multiply this result by
.93. to put it on the same scale as ERA if you prefer that. Verlander's BsR9 was slightly lower than his actual 3.06 runs allowed per nine innings (RA) which
indicates that he may have pitched a little better than his RA (or ERA) suggested.